Expectancy Calculator
Estimate the average rupee and R outcome per trade from your win rate, average win and average loss.
Quick answer: Expectancy is the average result you can expect per trade over many trades. It weights the average win by the probability of winning and subtracts the average loss weighted by the probability of losing. A positive expectancy means the system makes money on average; the tool also expresses the figure in R, where one R is the size of the average loss.
How to use it
Enter your historical win rate and the average rupee size of a winning and a losing trade. The output is the expected rupee value per trade and the same figure in R, where one R equals the average loss. A positive number means the system gains on average; a negative number means it bleeds even if the win rate looks high.
Formula
Expectancy = (Win% ÷ 100 × Average win) − (1 − Win% ÷ 100) × Average loss
Expressed in R by dividing by the average loss, so R normalises the result to units of typical risk.
Limitations — what this calculator does not model
- Uses single average win and average loss values, so it hides the distribution — a few large outliers can dominate the real result.
- Assumes the win rate and average sizes are stable and drawn from enough trades; small or in-sample samples overstate the edge.
- A positive expectancy does not bound risk of ruin — position sizing and losing streaks still decide survival.
- Feed post-cost figures: gross inputs flatter a thin edge that realistic Indian charges can turn negative.
Frequently asked questions
What does expectancy in R mean?
R is the average loss treated as one unit of risk. An expectancy of 0.35R means that, on average, each trade returns 35 percent of a typical losing trade. It lets you compare systems that trade different rupee amounts.
Can a high win rate still lose money?
Yes. If wins are small and losses are large, a 70 percent win rate can still produce negative expectancy. Expectancy captures both frequency and size, which raw win rate alone does not.
Should a live system monitor expectancy as it trades?
Yes. A common operational guardrail is to track rolling live expectancy against the backtested figure; when live expectancy decays materially below expectation, the system flags the strategy for reduced allocation or review, because edge decay usually shows up here first.
How does expectancy feed position sizing in a system?
Expectancy is a precondition for sizing, not a sizing rule itself. A stable, positive expectancy is what justifies risking capital at all; sizing methods such as fractional Kelly then use the edge and odds to set the fraction, and they are dangerous if the expectancy estimate is overstated.
Should the inputs come from gross or net trades?
Net of all costs — STT, brokerage, GST, stamp duty and slippage. A thin positive gross expectancy can become zero or negative once realistic Indian transaction costs are subtracted, so an ops team should always feed post-cost trade results.
Why can a live expectancy differ from the one I measured earlier?
Because edges decay or vanish as conditions change or a trade becomes crowded, and because slippage and partial fills erode the realised edge that a clean estimate assumed. Live expectancy is the reality check on every number that came before deployment.
Runs entirely in your browser — no data leaves your device. Illustrative and educational only; real-world charges and market conditions apply in practice.